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Counter intuitive learning: An exploratory study

Author

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  • Nobuyuki Hanaki

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Alan Kirman

    (CAMS - Centre d'Analyse et de Mathématique sociales - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Paul Pezanis-Christou

    (University of Adelaide)

Abstract

The literature on learning in unknown environments emphasises reinforcing on actions which produce positive results. But, in some cases, success requires shifting from a currently successful actions to others. We examine, experimentally and theoretically in a very simple framework, how individuals initially learn by exploiting information from the pay-offs of actions taken but also from exploring new actions. We analyse if and how they learn that pay-offs are inter-temporally dependent. We then ran the same experiments but where individuals could observe the actions taken or the pay-offs obtained by others or both. Such observations improved pay-offs if one of the pair had learned to obtain the maximum pay-off.

Suggested Citation

  • Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2016. "Counter intuitive learning: An exploratory study," Working Papers hal-01358716, HAL.
    • Handle: RePEc:hal:wpaper:hal-01358716
    Note: View the original document on HAL open archive server: https://hal.science/hal-01358716
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    References listed on IDEAS

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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    3. Ralph-C. Bayer & Hang Wu, 2013. "Do We Learn from Our Own Experience or from Observing Others?," School of Economics and Public Policy Working Papers 2013-21, University of Adelaide, School of Economics and Public Policy.
    4. Sonsino, Doron, 1997. "Learning to Learn, Pattern Recognition, and Nash Equilibrium," Games and Economic Behavior, Elsevier, vol. 18(2), pages 286-331, February.
    5. Bossan, Benjamin & Jann, Ole & Hammerstein, Peter, 2015. "The evolution of social learning and its economic consequences," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 266-288.
    6. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    7. Jeffrey Banks & David Porter & Mark Olson, 1997. "An experimental analysis of the bandit problem," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 10(1), pages 55-77.
    8. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
    9. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    10. Woodford, Michael, 1990. "Learning to Believe in Sunspots," Econometrica, Econometric Society, vol. 58(2), pages 277-307, March.
    11. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    12. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    More about this item

    Keywords

    multi-armed bandit; reinforcement learning; eureka moment; pay-off patterns; observational learning;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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